Detection of COVID-19 Disease with Machine Learning Algorithms from CT Images

Abstract

COVID-19, caused by the SARS-COV-2 virus, which has killed more than 6 million people, is one of the most contagious diseases in human history. It has seriously affected every area that people come into contact with, from business life to economy, from transportation to education, from social life to psychology. Although the developed vaccines provide a partial decrease in the number of deaths, the mutations that the virus constantly undergoes and the increase in the transmission rate accordingly reduce the effectiveness of the vaccines, and the number of deaths tends to increase as the number of infected people. It is undoubtedly important that the detection of this epidemic disease, which is the biggest crisis that humanity has experienced in the last century after World War II, is carried out accurately and quickly. In this study, a machine learning-based artificial intelligence method has been proposed for the detection of COVID-19 from computed tomography images. The features of images with two classes are extracted using the Local Binary Pattern. The images reserved for training in the dataset were used for training machine learning models. Trained models were tested with previously unused test images. While the Fine K-Nearest Neighbors model reached the highest accuracy with a value of 0.984 for the training images, the highest accuracy value was obtained by the Cubic Support Vector Machine with 0.93 for the test images. These results are higher than the deep learning-based study using the same data set.

Keywords

• Classification
• Machine learning
• Local binary pattern
• Computerized tomography
• COVID-19

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